





"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class       a							:



 def __init__(     self				,       _snake_case=2				,       _snake_case=3				,       _snake_case=64				,       _snake_case=None     ):







  """simple docstring"""






  lowerCAmelCase    				= np.random.default_rng(SCREAMING_SNAKE_CASE_     )
  lowerCAmelCase    				= length
  lowerCAmelCase    				= rng.normal(size=(length,)     ).astype(np.floataa     )
  lowerCAmelCase    				= a * self.x + b + rng.normal(scale=0.1				,       size=(length,)     ).astype(np.floataa     )



 def __len__(     self     ):







  """simple docstring"""






  return self.length



 def __getitem__(     self				,       _snake_case     ):







  """simple docstring"""






  return {"x": self.x[i], "y": self.y[i]}







class       a							(   torch.nn.Module  ):



 def __init__(     self				,       _snake_case=0				,       _snake_case=0				,       _snake_case=False     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
  lowerCAmelCase    				= torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
  lowerCAmelCase    				= True



 def    UpperCamelCase__  (     self				,       _snake_case=None     ):







  """simple docstring"""






  if self.first_batch:
   print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'     )
   lowerCAmelCase    				= False
  return x * self.a[0] + self.b[0]







class       a							(   torch.nn.Module  ):



 def __init__(     self				,       _snake_case=0				,       _snake_case=0				,       _snake_case=False     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_     ).float()     )
  lowerCAmelCase    				= torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_     ).float()     )
  lowerCAmelCase    				= True



 def    UpperCamelCase__  (     self				,       _snake_case=None     ):







  """simple docstring"""






  if self.first_batch:
   print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'     )
   lowerCAmelCase    				= False
  return x * self.a + self.b


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Dict  ,     _UpperCAmelCase					:    int = 16			):
 from datasets import load_dataset
 from transformers import AutoTokenizer

 lowerCAmelCase    				= AutoTokenizer.from_pretrained('bert-base-cased'			)
 lowerCAmelCase    				= {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
 lowerCAmelCase    				= load_dataset('csv'  ,     data_files=_lowerCamelCase			)
 lowerCAmelCase    				= datasets['train'].unique('label'			)

 lowerCAmelCase    				= {v: i for i, v in enumerate(_lowerCamelCase			)}

 def tokenize_function(_UpperCAmelCase					:    List[str]			):
  # max_length=None => use the model max length (it's actually the default)
  lowerCAmelCase    				= tokenizer(
      examples['sentence1']  ,     examples['sentence2']  ,     truncation=_lowerCamelCase  ,     max_length=_lowerCamelCase  ,     padding='max_length'			)
  if "label" in examples:
   lowerCAmelCase    				= [label_to_id[l] for l in examples['label']]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 lowerCAmelCase    				= datasets.map(
     _lowerCamelCase  ,     batched=_lowerCamelCase  ,     remove_columns=['sentence1', 'sentence2', 'label']  ,     )

 def collate_fn(_UpperCAmelCase					:    Tuple			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(_lowerCamelCase  ,     padding='max_length'  ,     max_length=128  ,     return_tensors='pt'			)
  return tokenizer.pad(_lowerCamelCase  ,     padding='longest'  ,     return_tensors='pt'			)

 # Instantiate dataloaders.
 lowerCAmelCase    				= DataLoader(tokenized_datasets['train']  ,     shuffle=_lowerCamelCase  ,     collate_fn=_lowerCamelCase  ,     batch_size=2			)
 lowerCAmelCase    				= DataLoader(tokenized_datasets['validation']  ,     shuffle=_lowerCamelCase  ,     collate_fn=_lowerCamelCase  ,     batch_size=1			)

 return train_dataloader, eval_dataloader







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						Dict:

	'''simple docstring'''




	def wrapper(*_lowerCamelCase:		Any,					**_lowerCamelCase:		Union[str, Any]			):
		lowerCAmelCase												=  timeit.default_timer()
		lowerCAmelCase												=  func(*_lowerCamelCase,					**_lowerCamelCase			)
		lowerCAmelCase												=  timeit.default_timer() - starttime
		return delta

	lowerCAmelCase												=  func.__name__

	return wrapper




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		List[Any]=100,					_lowerCamelCase:		int=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  seq_shapes or {}
	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  {}
		for col_id, (k, v) in enumerate(features.items()			):
			if isinstance(_lowerCamelCase,					_ArrayXD			):
				lowerCAmelCase												=  np.random.rand(*v.shape			).astype(v.dtype			)
			elif isinstance(_lowerCamelCase,					datasets.Value			):
				if v.dtype == "string":
					lowerCAmelCase												=  '''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCAmelCase												=  np.random.randint(10,					size=1			).astype(v.dtype			).item()
			elif isinstance(_lowerCamelCase,					datasets.Sequence			):
				while isinstance(_lowerCamelCase,					datasets.Sequence			):
					lowerCAmelCase												=  v.feature
				lowerCAmelCase												=  seq_shapes[k]
				lowerCAmelCase												=  np.random.rand(*_lowerCamelCase			).astype(v.dtype			)
			lowerCAmelCase												=  data

		dummy_data.append((i, example)			)

	return dummy_data




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Union[str, Any]=100,					_lowerCamelCase:		List[Any]=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  generate_examples(_lowerCamelCase,					num_examples=_lowerCamelCase,					seq_shapes=_lowerCamelCase			)

	with ArrowWriter(features=_lowerCamelCase,					path=_lowerCamelCase			) as writer:
		for key, record in dummy_data:
			lowerCAmelCase												=  features.encode_example(_lowerCamelCase			)
			writer.write(_lowerCamelCase			)

		lowerCAmelCase							,       lowerCAmelCase												=  writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""			)

	lowerCAmelCase												=  datasets.Dataset.from_file(filename=_lowerCamelCase,					info=datasets.DatasetInfo(features=_lowerCamelCase			)			)

	return dataset


